Prognostic utility of serial procalcitonin measurements in ICU sepsis: a laboratory-led modelling approach in a resource-limited setting procalcitonin modelling for sepsis mortality in a low-resource ICU

Aaqilah Fataar; Annalise E. Zemlin; Elsie C. Kruger

doi:10.1515/cclm-2025-0778

Article

Prognostic utility of serial procalcitonin measurements in ICU sepsis: a laboratory-led modelling approach in a resource-limited setting procalcitonin modelling for sepsis mortality in a low-resource ICU

Aaqilah Fataar , Annalise E. Zemlin and Elsie C. Kruger

Published/Copyright: November 12, 2025

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From the journal Clinical Chemistry and Laboratory Medicine (CCLM)

Abstract

Objectives

Procalcitonin (PCT) is increasingly used to support sepsis diagnosis, but its role in predicting outcomes remains uncertain, particularly in low-resource settings. We evaluated whether single and serial PCT measurements were associated with 28-day mortality in critically ill adults with suspected sepsis.

Methods

We conducted a retrospective study of adult intensive care unit (ICU) patients with suspected sepsis and at least one PCT measurement at a tertiary hospital in South Africa (August 2022-July 2023). Baseline PCT was analysed using multivariable logistic regression. Among patients with ≥2 PCT values, we examined the association between PCT slope (from patient-level linear regression) and mortality. Latent class mixed models (LCMM) were used to identify PCT trajectory subgroups.

Results

Of 371 patients, 119 (32 %) died within 28 days. Higher baseline log-PCT was independently associated with increased mortality (adjusted odds ratio [aOR] 1.58; 95 % CI 1.01–2.50). A rising PCT slope trended toward higher mortality (aOR 3.56 per unit/day; p=0.06). LCMM identified three trajectory classes with distinct mortality risks (class 2: aOR 4.53; class 3: aOR 4.35, vs. reference). These models were based entirely on laboratory data and did not assess clinical scoring systems.

Conclusions

Both baseline and serial PCT measurements predicted mortality in ICU patients with suspected sepsis. Modelling approaches based on routine laboratory data may offer scalable tools for early risk stratification in resource-limited settings.

Keywords: biomarkers; critical care; mortality prediction; trajectory analysis

Corresponding author: Dr. Aaqilah Fataar, Division of Chemical Pathology, Department of Pathology, Stellenbosch University, Francie van Zijl Drive, Cape Town, 7505, South Africa; and National Health Laboratory Service, Division of Chemical Pathology, Tygerberg Hospital, Cape Town, South Africa, E-mail: aaqilah.fataar@gmail.com

Research ethics: This study was approved by the Stellenbosch University Health Research Ethics Committee (Ref: S23/11/290) on 16 February 2024. The need for individual informed consent was waived due to the retrospective design and use of de-identified data.
Informed consent: Not applicable.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: The authors state no conflict of interest.
Research funding: None declared.
Data availability: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0778).

Received: 2025-07-03

Accepted: 2025-08-20

Published Online: 2025-11-12

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Supplementary Material

https://doi.org/10.1515/cclm-2025-0778

Keywords for this article

biomarkers; critical care; mortality prediction; trajectory analysis